{"id":"W2282534318","doi":"10.1002/cjce.22419","title":"Economic MPC of deep cone thickeners in coal beneficiation","year":2015,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Model predictive control; Beneficiation; Robustness (evolution); Control theory (sociology); Controller (irrigation); Coal; Cone (formal languages); Computer science; Volumetric flow rate; Mathematics; Control (management); Engineering; Artificial intelligence; Waste management; Algorithm; Materials science; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003145683,0.00008139574,0.0001905589,0.000141492,0.00000707051,0.00001227759,0.0001479205,0.00006640759,0.000004370157],"category_scores_gemma":[0.0001384717,0.00007510116,0.00003565704,0.00008028145,0.00001664963,0.0001208396,0.00000371608,0.0001890576,0.000001750753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006317052,"about_ca_system_score_gemma":0.0001384254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004782227,"about_ca_topic_score_gemma":0.001094728,"domain_scores_codex":[0.9993526,0.000009357182,0.0003548924,0.00003906541,0.00008055515,0.0001635563],"domain_scores_gemma":[0.9995327,0.00005841913,0.00007834942,0.00008218644,0.00005343737,0.0001948943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005198558,7.270691e-7,0.0001804514,0.00001006959,0.00001652445,0.000003496387,0.0002295115,0.9925082,0.006479014,0.000368633,0.00001900059,0.0001791113],"study_design_scores_gemma":[0.0006075668,0.00001351125,0.0002004614,0.00006282187,0.00001251952,0.0000392287,0.00004260603,0.9811345,0.01747119,0.0001412807,0.0001638668,0.000110426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655367,0.001414652,0.0315799,0.0001549747,0.0006723849,0.0001113745,0.000006162271,0.0000236293,0.0005002122],"genre_scores_gemma":[0.9991089,0.000002533009,0.0007349825,0.000007398348,0.0001218459,0.00000157183,0.000001649339,0.00001961704,0.000001501163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03357219,"threshold_uncertainty_score":0.3062536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0063214276855414,"score_gpt":0.1754464608340818,"score_spread":0.1691250331485404,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}